Classification with a combined information test

We introduce a discrete model for classifying a target that combines the information in training and test data to infer about the true symbol probabilities. Two tests are derived given that the symbols are distributed as a multinomial. The robustness of these tests lies in their ability to effectively use all of the information in the training and test data before making a classification decision. This is demonstrated by comparing their performance to a standard hypothesis test for a classification problem involving transmission of quantized data to a fusion center.